The complex computer models we use to predict the weather be used to predict illness, too. According to a new paper, these models could help us know weeks in advance just how bad this flu season will be.

The technique uses data taken from Google flu trends. This is near real-time data of people's searches for terms related to the flu, available down to district, province, state, or municipality in 28 different countries. Researchers run this information though weather forecast software that analyzes complex, real-time data. They write:

Here we apply a data assimilation method called the ensemble adjustment Kalman filter (EAKF) (20) to entrain weekly GFT estimates of ILI into a simple humidity-forced susceptible– infectious–recovered–susceptible (SIRS) mathematical model of influenza. The EAKF is a recursive filtering technique that combines observations with a temporally evolving ensemble of model simulations to generate a posterior estimate of the model state. This process nudges the ensemble mean toward the observations and simultaneously contracts the ensemble variance, thus constraining the model state and parameters.

The team retroactively used the data from the 2002-2008 flu seasons in New York City, and were able to predict the season's peak some seven weeks in advance.

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In a release, Dr. Shaman of Columbia University's Mailman School of Public Health explained how this information could be used:

In the future, such flu forecasts might conceivably be disseminated on the local television news along with the weather report. Because we are all familiar with weather broadcasts, when we hear that there is a 80% chance of rain, we all have an intuitive sense of whether or not we should carry an umbrella. I expect we will develop a similar comfort level and confidence in flu forecasts and develop an intuition of what we should do to protect ourselves in response to different forecast outcomes."

Since different areas peak at different times, this information could be used locally to provide the best prevention for individual cities and regions. So in the future, your evening news might end up telling you how sick you could end up getting, too.